Fit a list of lm or glm objects with a
common model for different subgroups of the data.
lmList(formula, data, family, subset, weights, na.action,
offset, pool = !isGLM || .hasScale(family2char(family)),
warn = TRUE, ...)an object of class
lmList4 (see
there, notably for the methods defined).
a linear formula object of the form
y ~ x1+...+xn | g. In the formula object, y
represents the response, x1,...,xn the covariates,
and g the grouping factor specifying the
partitioning of the data according to which different
lm fits should be performed.
an optional family specification for a
generalized linear model (glm).
an optional data frame containing the
variables named in formula. By default the
variables are taken from the environment from which
lmer is called. See Details.
an optional expression indicating the
subset of the rows of data that should be used in
the fit. This can be a logical vector, or a numeric
vector indicating which observation numbers are to be
included, or a character vector of the row names to be
included. All observations are included by default.
an optional vector of ‘prior
weights’ to be used in the fitting process. Should be
NULL or a numeric vector.
a function that indicates what should
happen when the data contain NAs. The default
action (na.omit, inherited from the ‘factory
fresh’ value of getOption("na.action")) strips any
observations with any missing values in any variables.
this can be used to specify an a
priori known component to be included in the linear
predictor during fitting. This should be NULL or a
numeric vector of length equal to the number of cases.
One or more offset terms can be included in
the formula instead or as well, and if more than one is
specified their sum is used. See
model.offset.
logical scalar indicating if the variance estimate should
pool the residual sums of squares. By default true if the model has
a scale parameter (which includes all linear, lmer(), ones).
indicating if errors in the single fits should signal a
“summary” warning.
additional, optional arguments to be passed to the model function or family evaluation.
While data is optional, the package authors
strongly recommend its use, especially when later applying
methods such as update and drop1 to the fitted model
(such methods are not guaranteed to work properly if
data is omitted). If data is omitted, variables will
be taken from the environment of formula (if specified as a
formula) or from the parent frame (if specified as a character vector).
Since lme4 version 1.1-16, if there are errors (see
stop) in the single (lm() or glm())
fits, they are summarized to a warning message which is returned as
attribute "warnMessage" and signalled as warning()
when the warn argument is true.
In previous lme4 versions, a general (different) warning had been signalled in this case.
lmList4
fm.plm <- lmList(Reaction ~ Days | Subject, sleepstudy)
coef(fm.plm)
fm.2 <- update(fm.plm, pool = FALSE)
## coefficients are the same, "pooled or unpooled":
stopifnot( all.equal(coef(fm.2), coef(fm.plm)) )
(ci <- confint(fm.plm)) # print and rather *see* :
plot(ci) # how widely they vary for the individuals
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